app.py

import gradio as gr
from transformers import AutoModel, AutoTokenizer
from sklearn.neighbors import NearestNeighbors

available_models = ['cardiffnlp/twitter-roberta-base-2019-90m',
                    'cardiffnlp/twitter-roberta-base-jun2020']

models = {}
tokenizers = {}

for MODEL in available_models:
    models[MODEL] = AutoModel.from_pretrained(MODEL)
    tokenizers[MODEL] = AutoTokenizer.from_pretrained(MODEL)


def topk_model(MODEL):
    # MODEL = "cardiffnlp/twitter-roberta-base-jun2022"
    # model = AutoModel.from_pretrained(MODEL)
    # tokenizer = AutoTokenizer.from_pretrained(MODEL)
    embedding_matrix = models[MODEL].embeddings.word_embeddings.weight
    embedding_matrix = embedding_matrix.detach().numpy()

    knn_model = NearestNeighbors(n_neighbors=500,
                            metric='cosine',
                            algorithm='auto',
                            n_jobs=3)
                    
    nbrs = knn_model.fit(embedding_matrix)

    distances, indices = nbrs.kneighbors(embedding_matrix)

    return distances,indices,tokenizers[MODEL]


title = "How does a word's meaning change with time?"

def topk(word,model):
    outs = []
    distances, indices, tokenizer = topk_model(model)

    index = tokenizer.encode(f'{word}')
    for i in indices[index[1]]:
        outs.append(tokenizer.decode(i))
        print(tokenizer.decode(i))

    return outs

# with gr.Blocks() as demo:
#     gr.Markdown(f" # {title}")
#     # gr.Markdown(f" ## {description1}")
#     # gr.Markdown(f"{description2}")
#     # gr.Markdown(f"{description3}")
#     with gr.Row():
#         word = gr.Textbox(label="Word")
#     with gr.Row():
#         greet_btn = gr.Button("Compute")
#     with gr.Row():
#         greet_btn.click(fn=topk, inputs=[word,gr.Dropdown(models)], outputs=gr.outputs.Textbox())
# demo.launch()

interface = gr.Interface(fn=topk, 
                        inputs=[gr.Textbox(label="Word"), gr.Dropdown(available_models)],
                        outputs=gr.outputs.Textbox()
                        )
interface.launch()